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III: Small: Moving offline learning to rank online, from theory to practice

三：小：把线下学习搬到线上排名，从理论到实践

基本信息

批准号：
2128019
负责人：
Shangtong Zhang
金额：
$ 50万
依托单位：
University of Virginia Main Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128019&HistoricalAwards=false
关键词：
III Small Moving offline learning

项目摘要

Online learning to rank is a modern machine learning technique that adaptively improves result rankings during its interactions with end users. For example, when applied in a search engine system, an online learning to rank solution can estimate the optimal ranking of results by repeating three steps: present a ranked list, collect user feedback (e.g., clicks), then update the ranking for next round of interaction. However, most existing online learning to rank solutions are extended from algorithms originally designed for online optimization, rather than the ranking problem; and thus, their practical performance is often much worse than their offline counterparts. This directly limits their practical acceptance. This project aims to develop a completely new online learning to rank framework, which directly converts the best practices in offline learning to rank online for improved performance and provable guarantees. The key innovation of this project is to break the exponentially large ranking space into quadratic-size pairwise comparisons on the fly, where online learning is only performed on the uncertain pairs of instance rankings. Built on this new online pairwise learning strategy, this project studies multi-objective optimization, collaborative and federated learning to enable online learning to rank in a wider range of application scenarios, such as fair and personalized online learning to rank. The research outcomes, including the developed algorithms, testbeds and evaluation protocols, will be disseminated via an open-source library. The research activities will be incorporated into teaching materials for student training and education in the area of information retrieval and machine learning. The planed outreach to high school students for education about online information techniques, privacy and fairness will increase their awareness of potential risk of privacy breaches and unfairness in online systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在线学习排名是一种现代机器学习技术，它在与最终用户的交互过程中自适应地提高结果排名。例如，当应用于搜索引擎系统时，在线学习排名解决方案可以通过重复三个步骤来估计结果的最佳排名：呈现排名列表，收集用户反馈（例如，点击），然后更新下一轮互动的排名。然而，大多数现有的在线学习排名解决方案是从最初设计用于在线优化的算法扩展而来的，而不是排名问题;因此，它们的实际性能往往比离线算法差得多。这直接限制了它们的实际接受程度。该项目旨在开发一个全新的在线学习排名框架，直接将离线学习中的最佳实践转换为在线排名，以提高性能和可证明的保证。该项目的关键创新是将指数级大的排名空间分解为动态的二次大小的成对比较，其中在线学习仅在不确定的实例排名对上执行。基于这种新的在线成对学习策略，该项目研究多目标优化、协作和联邦学习，使在线学习在更广泛的应用场景中排名，如公平和个性化的在线学习排名。研究成果，包括开发的算法、测试平台和评估协议，将通过一个开放源码图书馆传播。这些研究活动将纳入信息检索和机器学习领域的学生培训和教育教材。该计划将向高中生提供在线信息技术、隐私和公平方面的教育，以提高他们对在线系统中隐私泄露和不公平的潜在风险的认识。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

DOI：
发表时间：
2021-10
期刊：
ArXiv
影响因子：
0
作者：
Chuanhao Li;Hongning Wang
通讯作者：
Chuanhao Li;Hongning Wang

Communication Efficient Federated Learning for Generalized Linear Bandits

DOI：
发表时间：
2022-02
期刊：
ArXiv
影响因子：
0
作者：
Chuanhao Li;Hongning Wang
通讯作者：
Chuanhao Li;Hongning Wang

Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment

在分布式异步环境中学习内核化上下文强盗

DOI：
发表时间：
2023
期刊：
International Conference on Learning Representation
影响因子：
0
作者：
Li, Chuanhao;Wang, Huazheng;Wang, Mengdi;Wang, Hongning
通讯作者：
Wang, Hongning

When Are Linear Stochastic Bandits Attackable?

线性随机强盗何时会受到攻击？

DOI：
发表时间：
2022
期刊：
Proceedings of the 39th International Conference on Machine Learning
影响因子：
0
作者：
Wang, Huazheng;Xu, Haifeng;Wang, Hongning
通讯作者：
Wang, Hongning

Graph Structural Attack by Perturbing Spectral Distance

DOI：
10.1145/3534678.3539435
发表时间：
2021-11
期刊：
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Lu Lin;Ethan Blaser;Hongning Wang
通讯作者：
Lu Lin;Ethan Blaser;Hongning Wang